Summary of Echoatt: Attend, Copy, Then Adjust For More Efficient Large Language Models, by Hossein Rajabzadeh et al.
EchoAtt: Attend, Copy, then Adjust for More Efficient Large Language Models
by Hossein Rajabzadeh, Aref Jafari, Aman Sharma, Benyamin Jami, Hyock Ju Kwon, Ali Ghodsi, Boxing Chen, Mehdi Rezagholizadeh
First submitted to arxiv on: 22 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed EchoAtt framework optimizes transformer-based Large Language Models (LLMs) by analyzing and leveraging the similarity of attention patterns across layers. This approach reduces computational requirements during inference and fine-tuning without compromising performance. By sharing attention matrices in less critical layers, EchoAtt improves inference speed by 15%, training speed by 25%, and reduces the number of parameters by approximately 4%. The framework also enhances zero-shot performance, making LLMs more practical for real-time and resource-limited applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EchoAtt is a new way to make Large Language Models work better. Right now, these models are really good at understanding language, but they use a lot of computer power to do it. EchoAtt finds patterns in how the model pays attention to different parts of text, and then shares those patterns between similar parts of the model. This makes the model faster and uses less computer power, without losing its ability to understand language. |
Keywords
» Artificial intelligence » Attention » Fine tuning » Inference » Transformer » Zero shot
